Dr Wanqing Tu wanqing.tu@durham.ac.uk
Associate Professor
Resource-Efficient Seamless Transitions For High-Performance Multi-hop UAV Multicasting
Tu, Wanqing
Authors
Abstract
Many UAV-related applications require group communications between UAVs to reliably and efficiently deliver rich media content as well as to extend line-of-sight coverage between sky and ground. This paper is among the first to study fast yet resource-efficient UAV transitions for aerial group communications while maintaining high group communication performance. We develop a set of novel analytic and algorithmic results to form the efficient transition formation (ETF) algorithm, in order to deal with different UAV transition scenarios in a group communication environment. The ETF algorithm first evaluates the seamlessness of a straight-line trajectory (SLT) by processing low-complexity computations (e.g., Euclidean distances) or fast checks with controlled traffic overheads. For a non-seamless SLT, the ETF algorithmm establishes a new trajectory consisting of a minimal number of seamless straight lines that join at specially selected locations to control mobile UAVs’ seamless travel distances. Our simulation studies quantify the performance gains that the ETF algorithm may achieve, outperforming compared studies by admitting 66% more traffic with reduced energy consumption and guaranteed communication performance for both transitional and non-transitional UAVs.
Citation
Tu, W. (2022). Resource-Efficient Seamless Transitions For High-Performance Multi-hop UAV Multicasting. Computer Networks, 213, Article 109051. https://doi.org/10.1016/j.comnet.2022.109051
Journal Article Type | Article |
---|---|
Acceptance Date | May 15, 2022 |
Online Publication Date | May 23, 2022 |
Publication Date | Aug 4, 2022 |
Deposit Date | May 17, 2022 |
Publicly Available Date | Jun 22, 2023 |
Journal | Computer Networks |
Print ISSN | 1389-1286 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 213 |
Article Number | 109051 |
DOI | https://doi.org/10.1016/j.comnet.2022.109051 |
Public URL | https://durham-repository.worktribe.com/output/1205965 |
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Copyright Statement
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
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